Graphs in R :)))
 Faculty of Economics and Business Zagreb
Neural Networks courses
Kod  Nazwa  Miejscowość  Czas trwania  Data Kursu  PHP  Cena szkolenia [Zdalne / Stacjonarne] 

appliedml  Applied Machine Learning  Olsztyn, ul. Gietkowska 6a  14 hours  pon., 20180205 09:00  10110PLN / 3564PLN  
mldt  Machine Learning and Deep Learning  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180207 09:00  25100PLN / 8356PLN  
pjn  Przetwarzanie języka naturalnego  Olsztyn, ul. Gietkowska 6a  7 hours  pt., 20180209 09:00  6000PLN / 2068PLN  
aiintrozero  From Zero to AI  Olsztyn, ul. Gietkowska 6a  35 hours  pon., 20180212 09:00  24900PLN / 8795PLN  
MLFWR1  Machine Learning Fundamentals with R  Olsztyn, ul. Gietkowska 6a  14 hours  śr., 20180214 09:00  7000PLN / 4000PLN  
aiint  Artificial Intelligence Overview  Olsztyn, ul. Gietkowska 6a  7 hours  pon., 20180219 09:00  3900PLN / 1750PLN  
mlbankingpython_  Machine Learning for Banking (with Python)  Olsztyn, ul. Gietkowska 6a  21 hours  pon., 20180219 09:00  15820PLN / 5544PLN  
neuralnet  Introduction to the use of neural networks  Olsztyn, ul. Gietkowska 6a  7 hours  czw., 20180222 09:00  4580PLN / 1597PLN  
Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Olsztyn, ul. Gietkowska 6a  28 hours  wt., 20180227 09:00  30980PLN / 10388PLN  
rneuralnet  Sieci Neuronowe w R  Olsztyn, ul. Gietkowska 6a  14 hours  wt., 20180227 09:00  7000PLN / 4000PLN  
iop  Inteligencja obliczeniowa w praktyce  Olsztyn, ul. Gietkowska 6a  7 hours  czw., 20180301 09:00  6000PLN / 2068PLN  
cntk  Using Computer Network ToolKit (CNTK)  Olsztyn, ul. Gietkowska 6a  28 hours  pon., 20180305 09:00  25020PLN / 8582PLN  
mlbankingr  Machine Learning for Banking (with R)  Olsztyn, ul. Gietkowska 6a  28 hours  pon., 20180312 09:00  15820PLN / 5794PLN  
deeplearning1  Introduction to Deep Learning  Olsztyn, ul. Gietkowska 6a  21 hours  wt., 20180313 09:00  18260PLN / 6283PLN  
mtdintob  Metody Inteligencji Obliczeniowej  Olsztyn, ul. Gietkowska 6a  7 hours  pt., 20180316 09:00  6000PLN / 2068PLN  
undnn  Understanding Deep Neural Networks  Olsztyn, ul. Gietkowska 6a  35 hours  pon., 20180319 09:00  70000PLN / 22462PLN  
sysagent  Systemy wieloagentowe  Olsztyn, ul. Gietkowska 6a  7 hours  wt., 20180320 09:00  6000PLN / 2068PLN  
aiauto  Artificial Intelligence in Automotive  Olsztyn, ul. Gietkowska 6a  14 hours  śr., 20180321 09:00  13800PLN / 4682PLN  
annmldt  Artificial Neural Networks, Machine Learning, Deep Thinking  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180321 09:00  12370PLN / 4498PLN  
mlintro  Introduction to Machine Learning  Olsztyn, ul. Gietkowska 6a  7 hours  wt., 20180327 09:00  5060PLN / 1783PLN  
appliedml  Applied Machine Learning  Olsztyn, ul. Gietkowska 6a  14 hours  czw., 20180329 09:00  10110PLN / 3564PLN  
pjn  Przetwarzanie języka naturalnego  Olsztyn, ul. Gietkowska 6a  7 hours  pon., 20180402 09:00  6000PLN / 2068PLN  
mldt  Machine Learning and Deep Learning  Olsztyn, ul. Gietkowska 6a  21 hours  pon., 20180402 09:00  25100PLN / 8356PLN  
MLFWR1  Machine Learning Fundamentals with R  Olsztyn, ul. Gietkowska 6a  14 hours  wt., 20180410 09:00  7000PLN / 4000PLN  
d2dbdpa  From Data to Decision with Big Data and Predictive Analytics  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180411 09:00  29220PLN / 9605PLN  
aiintrozero  From Zero to AI  Olsztyn, ul. Gietkowska 6a  35 hours  pon., 20180416 09:00  24900PLN / 8795PLN  
neuralnet  Introduction to the use of neural networks  Olsztyn, ul. Gietkowska 6a  7 hours  śr., 20180418 09:00  4580PLN / 1597PLN  
iop  Inteligencja obliczeniowa w praktyce  Olsztyn, ul. Gietkowska 6a  7 hours  pon., 20180423 09:00  6000PLN / 2068PLN  
Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Olsztyn, ul. Gietkowska 6a  28 hours  pon., 20180423 09:00  30980PLN / 10388PLN  
aiint  Artificial Intelligence Overview  Olsztyn, ul. Gietkowska 6a  7 hours  czw., 20180426 09:00  3900PLN / 1750PLN  
cntk  Using Computer Network ToolKit (CNTK)  Olsztyn, ul. Gietkowska 6a  28 hours  wt., 20180501 09:00  25020PLN / 8582PLN  
rneuralnet  Sieci Neuronowe w R  Olsztyn, ul. Gietkowska 6a  14 hours  wt., 20180501 09:00  7000PLN / 4000PLN  
mtdintob  Metody Inteligencji Obliczeniowej  Olsztyn, ul. Gietkowska 6a  7 hours  pt., 20180504 09:00  6000PLN / 2068PLN  
mlbankingpython_  Machine Learning for Banking (with Python)  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180509 09:00  15820PLN / 5544PLN  
deeplearning1  Introduction to Deep Learning  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180509 09:00  18260PLN / 6283PLN  
aiauto  Artificial Intelligence in Automotive  Olsztyn, ul. Gietkowska 6a  14 hours  czw., 20180510 09:00  13800PLN / 4682PLN  
sysagent  Systemy wieloagentowe  Olsztyn, ul. Gietkowska 6a  7 hours  pt., 20180511 09:00  6000PLN / 2068PLN  
undnn  Understanding Deep Neural Networks  Olsztyn, ul. Gietkowska 6a  35 hours  pon., 20180514 09:00  70000PLN / 22462PLN  
annmldt  Artificial Neural Networks, Machine Learning, Deep Thinking  Olsztyn, ul. Gietkowska 6a  21 hours  pon., 20180514 09:00  12370PLN / 4498PLN  
mlbankingr  Machine Learning for Banking (with R)  Olsztyn, ul. Gietkowska 6a  28 hours  pon., 20180514 09:00  15820PLN / 5794PLN  
mlintro  Introduction to Machine Learning  Olsztyn, ul. Gietkowska 6a  7 hours  czw., 20180517 09:00  5060PLN / 1783PLN  
appliedml  Applied Machine Learning  Olsztyn, ul. Gietkowska 6a  14 hours  pon., 20180521 09:00  10110PLN / 3564PLN  
pjn  Przetwarzanie języka naturalnego  Olsztyn, ul. Gietkowska 6a  7 hours  śr., 20180523 09:00  6000PLN / 2068PLN  
mldt  Machine Learning and Deep Learning  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180523 09:00  25100PLN / 8356PLN  
MLFWR1  Machine Learning Fundamentals with R  Olsztyn, ul. Gietkowska 6a  14 hours  czw., 20180531 09:00  7000PLN / 4000PLN  
d2dbdpa  From Data to Decision with Big Data and Predictive Analytics  Olsztyn, ul. Gietkowska 6a  21 hours  wt., 20180605 09:00  29220PLN / 9605PLN  
neuralnet  Introduction to the use of neural networks  Olsztyn, ul. Gietkowska 6a  7 hours  czw., 20180607 09:00  4580PLN / 1597PLN  
aiintrozero  From Zero to AI  Olsztyn, ul. Gietkowska 6a  35 hours  pon., 20180611 09:00  24900PLN / 8795PLN  
iop  Inteligencja obliczeniowa w praktyce  Olsztyn, ul. Gietkowska 6a  7 hours  pon., 20180611 09:00  6000PLN / 2068PLN  
aiint  Artificial Intelligence Overview  Olsztyn, ul. Gietkowska 6a  7 hours  pt., 20180615 09:00  3900PLN / 1750PLN  
Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Olsztyn, ul. Gietkowska 6a  28 hours  wt., 20180619 09:00  30980PLN / 10388PLN  
mtdintob  Metody Inteligencji Obliczeniowej  Olsztyn, ul. Gietkowska 6a  7 hours  pt., 20180622 09:00  6000PLN / 2068PLN  
cntk  Using Computer Network ToolKit (CNTK)  Olsztyn, ul. Gietkowska 6a  28 hours  pon., 20180625 09:00  25020PLN / 8582PLN  
rneuralnet  Sieci Neuronowe w R  Olsztyn, ul. Gietkowska 6a  14 hours  pon., 20180625 09:00  7000PLN / 4000PLN  
sysagent  Systemy wieloagentowe  Olsztyn, ul. Gietkowska 6a  7 hours  pon., 20180702 09:00  6000PLN / 2068PLN  
mlbankingpython_  Machine Learning for Banking (with Python)  Olsztyn, ul. Gietkowska 6a  21 hours  wt., 20180703 09:00  15820PLN / 5544PLN  
deeplearning1  Introduction to Deep Learning  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180704 09:00  18260PLN / 6283PLN  
aiauto  Artificial Intelligence in Automotive  Olsztyn, ul. Gietkowska 6a  14 hours  śr., 20180704 09:00  13800PLN / 4682PLN  
annmldt  Artificial Neural Networks, Machine Learning, Deep Thinking  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180704 09:00  12370PLN / 4498PLN  
mlintro  Introduction to Machine Learning  Olsztyn, ul. Gietkowska 6a  7 hours  czw., 20180705 09:00  5060PLN / 1783PLN  
mlbankingr  Machine Learning for Banking (with R)  Olsztyn, ul. Gietkowska 6a  28 hours  pon., 20180709 09:00  15820PLN / 5794PLN  
undnn  Understanding Deep Neural Networks  Olsztyn, ul. Gietkowska 6a  35 hours  pon., 20180709 09:00  70000PLN / 22462PLN  
appliedml  Applied Machine Learning  Olsztyn, ul. Gietkowska 6a  14 hours  śr., 20180711 09:00  10110PLN / 3564PLN  
pjn  Przetwarzanie języka naturalnego  Olsztyn, ul. Gietkowska 6a  7 hours  pt., 20180713 09:00  6000PLN / 2068PLN  
mldt  Machine Learning and Deep Learning  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180718 09:00  25100PLN / 8356PLN  
neuralnet  Introduction to the use of neural networks  Olsztyn, ul. Gietkowska 6a  7 hours  śr., 20180801 09:00  4580PLN / 1597PLN  
d2dbdpa  From Data to Decision with Big Data and Predictive Analytics  Olsztyn, ul. Gietkowska 6a  21 hours  śr., 20180801 09:00  29220PLN / 9605PLN  
iop  Inteligencja obliczeniowa w praktyce  Olsztyn, ul. Gietkowska 6a  7 hours  czw., 20180802 09:00  6000PLN / 2068PLN  
MLFWR1  Machine Learning Fundamentals with R  Olsztyn, ul. Gietkowska 6a  14 hours  pon., 20180806 09:00  7000PLN / 4000PLN 
Kod  Nazwa  Czas trwania  Spis treści 

cntk  Using Computer Network ToolKit (CNTK)  28 hours 
Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multimachine, MultiGPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects. Getting started
Configuring CNTK
Describing Networks
Data readers
Evaluating CNTK Models
Advanced topics
¹ The topic related to the use of CNTK with a GPU is not available as a part of a remote course. This module can be delivered during classroombased courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware. 
encogadv  Encog: Advanced Machine Learning  14 hours 
Encog is an opensource machine learning framework for Java and .Net. In this instructorled, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models. By the end of this training, participants will be able to:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
aiintrozero  From Zero to AI  35 hours 
This course is created for people who have no previous experience in probability and statistics. Probability (3.5h)
Statistics (10.5h)
Intro to programming (3.5h)
Machine Learning (10.5h)
Rules Engines and Expert Systems (7 hours)

matlabdl  Matlab for Deep Learning  14 hours 
In this instructorled, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
aiauto  Artificial Intelligence in Automotive  14 hours 
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making. Current state of the technology
Rules based AI
Machine Learning
Deep Learning
Deep Learning in practice (mainly using TensorFlow)
Sample usage

mlbankingr  Machine Learning for Banking (with R)  28 hours 
In this instructorled, live training, participants will learn how to apply machine learning techniques and tools for solving realworld problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience
Format of the course
Introduction
Different Types of Machine Learning
Machine Learning Languages and Toolsets
Machine Learning Case Studies
Introduction to R
How to Load Machine Learning Data
Modeling Business Decisions with Supervised Learning
Regression Analysis
Classification
Handson: Building an Estimation Model
Evaluating the performance of Machine Learning Algorithms
Modeling Business Decisions with Unsupervised Learning
Handson: Building a Recommendation System
Extending your company's capabilities
Closing Remarks 
aiint  Artificial Intelligence Overview  7 hours 
Kurs ten został stworzony dla menadżerów, architektów, analityków biznesowych i systemowych, menedżerów oprogramowania oraz wszystkich zainteresowanych przeglądem stosowania sztucznej inteligencji i prognozą dla jej rozwoju. Artificial Intelligence History
Problem Solving
Knowledge and Reasoning
Uncertain Knowledge and Reasoning
Learning
Communicating, Perceiving, and Acting;
Conclusions

Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  28 hours 
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. TensorFlow Basics
TensorFlow Mechanics
The Perceptron
From the Perceptron to Support Vector Machines
Artificial Neural Networks
Convolutional Neural Networks

mlbankingpython_  Machine Learning for Banking (with Python)  21 hours 
In this instructorled, live training, participants will learn how to apply machine learning techniques and tools for solving realworld problems in the banking industry. Python will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
Format of the course
Introduction
Different Types of Machine Learning
Machine Learning Languages and Toolsets
Machine Learning Case Studies
Handson: Python for Machine Learning
How to Load Machine Learning Data
Modeling Business Decisions with Supervised Learning
Regression Analysis
Classification
Handson: Building an Estimation Model
Evaluating the performance of Machine Learning Algorithms
Modeling Business Decisions with Unsupervised Learning
Handson: Building a Recommendation System
Extending your company's capabilities
Closing Remarks 
neuralnet  Introduction to the use of neural networks  7 hours 
Szkolenie skierowane jest do osób, które chcą zapoznać się z podstawami sieci neuronowych oraz ich zastosowań. Podstawy
Sieci neuronowe
Problemy na dziś

datamodeling  Pattern Recognition  35 hours 
This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of handson exercises, instructor feedback, and testing of knowledge and skills acquired. Audience
Introduction Probability theory, model selection, decision and information theory Probability distributions Linear models for regression and classification Neural networks Kernel methods Sparse kernel machines Graphical models Mixture models and EM Approximate inference Sampling methods Continuous latent variables Sequential data Combining models

undnn  Understanding Deep Neural Networks  35 hours 
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). Part1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part2(20%) of this training introduces Theano  a python library that makes writing deep learning models easy. Part3(40%) of the training would be extensively based on Tensorflow  2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will:
Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject. The Duration of the complete course will be around 70 hours and not 35 hours. Part 1 – Deep Learning and DNN Concepts
Basic Concepts of a Neural Network (Application: multilayer perceptron)
Standard ML / DL Tools A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
Convolutional Neural Networks (CNN).
Recurrent Neural Networks (RNN).
Deep Reinforcement Learning.
Part 2 – Theano for Deep Learning Theano Basics
Theano Functions
Training and Optimization of a neural network using Theano
Testing the model
TensorFlow Basics
TensorFlow Mechanics
The Perceptron
From the Perceptron to Support Vector Machines
Artificial Neural Networks
Convolutional Neural Networks
Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability): Tensorflow  Advanced Usage

rneuralnet  Sieci Neuronowe w R  14 hours 
Szkolenie jest wprowadzeniem do wdrożenia sieci neuronowych w życiu codziennym wykorzystując oprogramowanie Rproject. Introduction to Neural Networks
Overview of packages available
Applying Neural Networks

Torch  Torch: Getting started with Machine and Deep Learning  21 hours 
Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this course we cover the principles of Torch, its unique features, and how it can be applied in realworld applications. We step through numerous handson exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects. Audience Format of the course Introduction to Torch Installing Torch Installing Torch packages Choosing an IDE for Torch Working with the Lua scripting language and LuaJIT Loading a dataset in Torch Machine Learning in Torch Image analysis with Torch Working with the REPL interpreter Working with databases Networking and Torch GPU support in Torch Integrating Torch Embedding Torch Other frameworks and libraries Creating your own package Testing and debugging Releasing your application The future of AI and Torch 
dlfornlp  Deep Learning for NLP (Natural Language Processing)  28 hours 
Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructorled, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions. By the end of this training, participants will be able to:
Audience
Format of the course
Introduction to Deep Learning for NLP Differentiating between the various types of DL models Using pretrained vs trained models Using word embeddings and sentiment analysis to extract meaning from text How Unsupervised Deep Learning works Installing and Setting Up Python Deep Learning libraries Using the Keras DL library on top of TensorFlow to allow Python to create captions Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) to use as extended DL libraries for the purpose of creating captions. Using Keras on top of TensorFlow or Theano to quickly experiment on Deep Learning Creating a simple Deep Learning application in TensorFlow to add captions to a collection of pictures Troubleshooting A word on other (specialized) DL frameworks Deploying your DL application Using GPUs to accelerate DL Closing remarks 
MLFWR1  Machine Learning Fundamentals with R  14 hours 
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. Introduction to Applied Machine Learning
Regression
Classification
Crossvalidation and Resampling
Unsupervised Learning

OpenNN  OpenNN: Implementing neural networks  14 hours 
OpenNN is an opensource class library written in C++ which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience Format of the course Introduction to OpenNN, Machine Learning and Deep Learning Downloading OpenNN Working with Neural Designer OpenNN architecture OpenNN classes Building a neural network application Working with datasets Learning tasks Compiling with QT Creator Integrating, testing and debugging your application The future of neural networks and OpenNN 
appliedml  Applied Machine Learning  14 hours 
This training course is for people that would like to apply Machine Learning in practical applications. AudienceThis course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.

mldt  Machine Learning and Deep Learning  21 hours 
This course covers AI (emphasizing Machine Learning and Deep Learning) Machine learningIntroduction to Machine Learning
Regression
Resampling Methods
Model Selection and Regularization
Classification
Introduction to Deep LearningANN Structure
Feed forward ANN.
Deep Learning
Lab:Getting Started with R
Regression
Classification
Note:

mlintro  Introduction to Machine Learning  7 hours 
This training course is for people that would like to apply basic Machine Learning techniques in practical applications. AudienceData scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience.

opennmt  OpenNMT: Setting up a Neural Machine Translation System  7 hours 
OpenNMT is a fullfeatured, opensource (MIT) neural machine translation system that utilizes the Torch mathematical toolkit. In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution. Source and target language samples will be prearranged per the audience's requirements. Audience
Format of the course
Introduction Overview of the Torch project Installation and setup Preprocessing your data Training the model Translating Using pretrained models Working with Lua scripts Using extensions Troubleshooting Joining the community Closing remarks 
d2dbdpa  From Data to Decision with Big Data and Predictive Analytics  21 hours 
AudienceIf you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery ModeDuring the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software usedAll software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. Quick Overview
Datatypes
Models
Data Classification
Predictive Models
ROI
Building Models
Overview of Open Source and commercial software

Fairsec  Fairsec: Setting up a CNNbased machine translation system  7 hours 
Fairseq is an opensource sequencetosequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements. Audience
Format of the course Introduction Overview of the Torch project Overview of a Convolutional Neural Machine Translation model Overview of training approaches Installation and setup Evaluating pretrained models Preprocessing your data Training the model Translating Converting a trained model to use CPUonly operations Joining to the community Closing remarks 
annmldt  Artificial Neural Networks, Machine Learning, Deep Thinking  21 hours 
DAY 1  ARTIFICIAL NEURAL NETWORKSIntroduction and ANN Structure.
Mathematical Foundations and Learning mechanisms.
Single layer perceptrons.
Feedforward ANN.
Radial Basis Function Networks.
Competitive Learning and Self organizing ANN.
Fuzzy Neural Networks.
Applications
DAY 2 MACHINE LEARNING
DAY 3  DEEP LEARNINGThis will be taught in relation to the topics covered on Day 1 and Day 2

Fairseq  Fairseq: Setting up a CNNbased machine translation system  7 hours 
Fairseq is an opensource sequencetosequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements. Audience
Format of the course Introduction Overview of the Torch project Overview of a Convolutional Neural Machine Translation model Overview of training approaches Installation and setup Evaluating pretrained models Preprocessing your data Training the model Translating Converting a trained model to use CPUonly operations Joining to the community Closing remarks 
deeplearning1  Introduction to Deep Learning  21 hours 
This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.

facebooknmt  Facebook NMT: Setting up a Neural Machine Translation System  7 hours 
Fairseq is an opensource sequencetosequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience
Format of the course
Note
Introduction Overview of the Torch and Caffe2 projects Overview of a Convolutional Neural Machine Translation model Overview of training approaches Installation and setup Evaluating pretrained models Preprocessing your data Training the model Translating Converting a trained model to use CPUonly operations Joining to the community Closing remarks 
sysagent  Systemy wieloagentowe  7 hours 
1. Wstęp systemy wieloagentowea. czym jest agent programowy b. rodzaje agentów c. platformawieloagentowa i społeczność agentów d. analogia do systemów żywych 2. Teoriaa. Architektury systemów wieloagentowych
b. Inteligencja agenta i interakcja z otoczeniem
c. Wybrane algorytmy społecznościowe

tpuprogramming  TPU Programming: Building Neural Network Applications on Tensor Processing Units  7 hours 
The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8bit integers instead of 16bit in order to return appropriate levels of precision. In this instructorled, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications. By the end of the training, participants will be able to:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
pjn  Przetwarzanie języka naturalnego  7 hours 
1. Wprowadzenie2. Zastosowania NLP
3. Podstawy języka Perl
4 . Podstawy narzędzi RDBMS
5. Wyszukiwarka dokumentów

MicrosoftCognitiveToolkit  Microsoft Cognitive Toolkit 2.x  21 hours 
Microsoft Cognitive Toolkit 2.x (previously CNTK) is an opensource, commercialgrade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 510x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for imagerelated tasks. In this instructorled, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercialgrade AI applications involving multiple types of data such data, speech, text, and images. By the end of this training, participants will be able to:
Audience
Format of the course
Note
To request a customized course outline for this training, please contact us. 
iop  Inteligencja obliczeniowa w praktyce  7 hours 
1. Obszary zastosowań
2. Surowe dane3. Przetwarzanie wstępne danych, sygnałów (np. normalizacja, PCA, FFT itp.)4. Dobór elementów do zbioru uczącego i testowego (np. walidacja krzyżowa)5. Wybór metody inteligencji obliczeniowej6. Optymalizacja parametrów treningu (np. algorytmy genetyczne)7. Ocena uzyskanych wyników (np. krzywa ROC)8. Przykładowe zastosowania MIO:

snorkel  Snorkel: Rapidly process training data  7 hours 
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain. In this instructorled, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel. By the end of this training, participants will be able to:
Audience
Format of the course
To request a customized course outline for this training, please contact us.

mtdintob  Metody Inteligencji Obliczeniowej  7 hours 
1. Wstęp Sztuczna inteligencjaa. słaba i silna sztuczna inteligencja b. sztuczna inteligencja a inteligencja obliczeniowa c. klasyfikacja metod inteligencji obliczeniowej d. analogie do systemów żywych 2. Metody inteligencji obliczenioweja. sztuczne sieci neuronowe
b. systemy rozmyte
c. maszyna wektorów nośnych
d. obliczenia ewolucyjne
e. inteligencja roju f. inteligentne agenty g. algorytm knajbliższych sąsiadów h. systemy hybrydowe

encogintro  Encog: Introduction to Machine Learning  14 hours 
Encog is an opensource machine learning framework for Java and .Net. In this instructorled, live training, participants will learn how to create various neural network components using ENCOG. Realworld case studies will be discussed and machine language based solutions to these problems will be explored. By the end of this training, participants will be able to:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 